Towards Explainable Traffic Flow Prediction with Large Language Models

2024/04/03 by Xusen Guo, Qiming Zhang, Guo, Xusen +6 · 8 citations
Decision Sciences · Engineering · #Artificial Intelligence (cs.AI) #Big Data Technologies and Applications #FOS: Computer and information sciences #Machine Learning (cs.LG) #Traffic Prediction and Management Techniques

paper · pdf · doi:10.48550/arxiv.2404.02937

openalex publication_date 2024/04/03 · openalex created_date 2024/04/06 · openalex updated_date 2026/07/28

Abstract

Traffic forecasting is crucial for intelligent transportation systems. It has experienced significant advancements thanks to the power of deep learning in capturing latent patterns of traffic data. However, recent deep-learning architectures require intricate model designs and lack an intuitive understanding of the mapping from input data to predicted results. Achieving both accuracy and explainability in traffic prediction models remains a challenge due to the complexity of traffic data and the inherent opacity of deep learning models. To tackle these challenges, we propose a Traffic flow Prediction model based on Large Language Models (LLMs) to generate explainable traffic predictions, named xTP-LLM. By transferring multi-modal traffic data into natural language descriptions, xTP-LLM captures complex time-series patterns and external factors from comprehensive traffic data. The LLM framework is fine-tuned using language-based instructions to align with spatial-temporal traffic flow data. Empirically, xTP-LLM shows competitive accuracy compared with deep learning baselines, while providing an intuitive and reliable explanation for predictions. This paper contributes to advancing explainable traffic prediction models and lays a foundation for future exploration of LLM applications in transportation. To the best of our knowledge, this is the first study to use LLM for explainable prediction of traffic flows.

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Related

• Embracing Large Language Models in Traffic Flow Forecasting
• Spatial-Temporal Large Language Model for Traffic Prediction
• TPLLM: A Traffic Prediction Framework Based on Pretrained Large Language Models
• FlowDistill: Scalable Traffic Flow Prediction via Distillation from LLMs
• Enhancing Traffic Prediction with Textual Data Using Large Language Models